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Бакалаврская программа «Программа двух дипломов НИУ ВШЭ и Лондонского университета "Прикладной анализ данных"»

05
Декабрь

Machine Learning 1

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
8
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 1-4 модуль

Преподаватели

Course Syllabus

Abstract

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods and much more. The two modules (Sept-Dec, 2020) use Python programming language and popular packages to investigate and visualize datasets and develop machine learning models. The next two modules (Jan - May, 2021) use R programming language to prepare students for the exam from the University of London (UoL) and London School of Economics (LSE), which will count towards the UoL degree of DBSA and ICEF students. Pre-requisites: at least one semester of calculus on a real line, vector calculus, linear algebra, probability and statistics, computer programming in high level language such as Python or R.
Learning Objectives

Learning Objectives

  • The course aims to help students develop an understanding of the process to learn from data, familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
Expected Learning Outcomes

Expected Learning Outcomes

  • Build features suitable for the selected machine learning models
  • Build and interpret the data visualizations in Python and R programming language
  • Construct machine learning models on the proposed data sets in R
  • Evaluate performance of the models
  • Tune models to improve prediction and classification performance of the models
Course Contents

Course Contents

  • Math Essentials. Intro to Python in Google Colab
  • Intro to Statistical learning
  • Simple Linear Regression (SLR)
  • Multiple Linear Regression (MLR), kNN
  • Classification: Logistic Regression
  • Classification: LDA, QDA, KNN
  • Resampling methods. CV, Bootstrap
  • Linear model selection & regularization
  • Non-linear regression
  • Decision Trees
  • Bagging, Random Forest, Boosting
  • Support Vector Machines/Classifiers
Assessment Elements

Assessment Elements

  • non-blocking Quizzes
    Все вопросы на английском языке.
  • non-blocking homework assignments
  • non-blocking Exam
    There will be exams at the end of each of the 4 modules. The examination locations are TBD. An in-class exam is closed book, notes, calculators and phones. Take-home exam is an open book/internet, but no collaboration. Exam questions are different from homework questions: HW deepens your understanding, but the exams measure it. Each exam is cumulative.
  • non-blocking Coursework Project (CP) in R programming language
    Administered by LSE/UoL
  • non-blocking Participation
  • non-blocking Tests
    There will be tests at the end of each of the 4 modules. The examination locations are TBD. An in-class test is closed book, notes, calculators and phones. Take-home test is an open book/internet, but no collaboration. Test questions are different from homework questions: HW deepens your understanding, but the tests measure it. Each test is cumulative. Do not book travel that conflicts with this date.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5* Exam
  • Interim assessment (4 module)
    0.35*Homework + 0.1*Quizzes + 0.05*Participation + 0.5*(Module1 + Test2 + Test3 +2*UOL Results)
Bibliography

Bibliography

Recommended Core Bibliography

  • Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, & Maintainer Trevor Hastie. (2013). Type Package Title Data for An Introduction to Statistical Learning with Applications in R Version 1.0. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.28D80286

Recommended Additional Bibliography

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The Elements of Statistical Learning : Data Mining, Inference, and Prediction (Vol. Second edition, corrected 7th printing). New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=277008